Machine based sign language interpreter

ABSTRACT

A computer implemented method for performing sign language translation based on movements of a user is provided. A capture device detects motions defining gestures and detected gestures are matched to signs. Successive signs are detected and compared to a grammar library to determine whether the signs assigned to gestures make sense relative to each other and to a grammar context. Each sign may be compared to previous and successive signs to determine whether the signs make sense relative to each other. The signs may further be compared to user demographic information and a contextual database to verify the accuracy of the translation. An output of the match between the movements and the sign is provided.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.12/794,455 filed on Jun. 4, 2010 entitled “MACHINE BASED SIGN LANGUAGEINTERPRETER”, published as US-2011-0301934A1 on Dec. 8, 2011.

BACKGROUND

Language translation systems have been developed in a number ofdifferent contexts. Technology exists to translate written language fromone language to another, and to display sign language motions to usersbased on selecting a specific meaning or word to be displayed.

Systems have been disclosed that utilize multiple sensors, such ascameras, to detect motion and gestures for purposes of controlling acomputer interface, such as a game.

SUMMARY

Technology is provided which transcribes sign language (communication bygestures) into written or auditory forms of communication. User gestures(usually using the hands) are detected and detected gestures are matchedto signs. Successive signs are detected and compared to a grammarlibrary to determine whether the signs assigned to gestures make senserelative to each other and to a grammar context. In one embodiment, acomputer implemented method for interpreting sign language is provided.A capture device captures a scene including a human target and theusers' body part movements (especially hands) within the scene aretracked. Gestures are detected and compared to gestures matched withsign language signs in a lexicon library. Each sign may be compared toprevious and successive signs to determine whether the signs make senserelative to each other. The signs may further be compared to userdemographic information and a contextual database to verify the accuracyof the translation. An output of the match between the movements and thesign is provided.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate an example embodiment of a tracking systemwith a user playing a game.

FIG. 2 illustrates an example embodiment of a capture device that may beused as part of the tracking system.

FIG. 3 depicts an example of a processing device that may be used totrack gestures and execute and application based on the tracked gesture.

FIG. 4 illustrates a second example embodiment of a computing systemthat may be used to track motion and update an application based on thetracked motion.

FIGS. 5A-5D are representations of various visual signs of American SignLanguage (ASL).

FIGS. 6A and 6B are representations of various visual signs of AmericanSign taken in different contexts.

FIG. 7 is a flowchart illustrating a method in accordance with thepresent technology.

FIG. 8 is a flowchart illustrating a method of comparing gestures to alibrary of known signs.

FIG. 9 is a flowchart illustrating a method of comparing signs toadjacent signs.

FIGS. 10A-10E are a representation of a gesture recognizer and skeletaltracking models for a user.

FIGS. 11-12 show exemplary display output and user interfaces used withthe system of the present technology.

DETAILED DESCRIPTION

Technology is provided for performing sign language translation based ona user's gestures. A capture device is used to detect user gestures anddetected gestures are matched to signs. Successive signs are detectedand compared to a grammar library to determine whether the signsassigned to gestures make sense relative to each other and to a lexiconcontext. Each sign may be compared to previous and successive signs todetermine whether the signs make sense relative to each other. The signsmay further be compared to user demographic information and a contextualdatabase to verify the accuracy of the translation. An output of thematch between the movements and the sign is provided.

FIG. 1A illustrates an example embodiment of a system 10 which can beused with the present technology. One use of the exemplary tracking andrendering system 10 is where a user 18 plays a game. Alternatively, asdiscussed herein, the system may be utilized for translation andinterpretation of sign language when the gestures of the user arerecognized as signs in one or more sign language.

In FIG. 1A the example is the user playing a boxing game. In an exampleembodiment, the system 10 may be used to recognize, analyze, and/ortrack a human target such as the user 18 or other objects within rangeof tracking system 10.

Tracking system 10 may include a computing system 12. The computingsystem 12 may be a computer, a gaming system or console, or the like.According to an example embodiment, the computing system 12 may includehardware components and/or software components such that computingsystem 12 may be used to execute applications such as gamingapplications, non-gaming applications, or the like. In one embodiment,computing system 12 may include a processor such as a standardizedprocessor, a specialized processor, a microprocessor, or the like thatmay execute instructions stored on a processor readable storage devicefor performing the processes described herein.

As shown in FIG. 1A, tracking and rendering system 10 may furtherinclude a capture device 20. The capture device 20 may be, for example,a camera that may be used to visually monitor one or more users, such asthe user 18, such that gestures and/or movements performed by the one ormore users may be captured, analyzed, and tracked to perform one or morecontrols or actions within the application and/or animate an avatar oron-screen character, as will be described in more detail below.

According to one embodiment, the tracking and rendering system 10 may beconnected to an audio/visual device 16 such as a television, a monitor,a high-definition television (HDTV), or the like that may provide gameor application visuals and/or audio to a user such as the user 18. Forexample, the computing system 12 may include a video adapter such as agraphics card and/or an audio adapter such as a sound card that mayprovide audio/visual signals associated with the game application,non-game application, or the like. The audio/visual device 16 mayreceive the audio/visual signals from the computing system 12 and maythen output the game or application visuals and/or audio associated withthe audio/visual signals to the user 18. According to one embodiment,the audio/visual device 16 may be connected to the computing system 12via, for example, an S-Video cable, a coaxial cable, an HDMI cable, aDVI cable, a VGA cable, component video cable, or the like.

As shown in FIG. 1A, the system 10 may be used to recognize, analyze,and/or track a human target such as the user 18. For example, the user18 may be tracked using the capture device 20 such that the gesturesand/or movements of user 18 may be captured to animate an avatar oron-screen character and/or may be interpreted as controls that may beused to affect the application being executed by computer environment12. Thus, according to one embodiment, the user 18 may move his or herbody to control the application and/or animate the avatar or on-screencharacter. Similarly, tracking system 10 may be used to recognize,analyze, and/or track persons who are watching user 18 play the game sothat movement by those persons watching user 18 play the game willcontrol movement avatars in the audience at the boxing game displayed onaudio/visual device 16.

In the example depicted in FIG. 1A, the application executing on thesystem 10 may be a boxing game that the user 18 is playing. For example,the computing system 12 may use the audio/visual device 16 to provide avisual representation of a boxing opponent 22 to the user 18. Thecomputing system 12 may also use the audio/visual device 16 to provide avisual representation of a user avatar 24 that the user 18 may controlwith his or her movements. For example, as shown in FIG. 1B, the user 18may throw a punch in physical space to cause the user avatar 24 to throwa punch in game space. Thus, according to an example embodiment, thecomputer system 12 and the capture device 20 recognize and analyze thepunch of the user 18 in physical space such that the punch may beinterpreted as a game control of the user avatar 24 in game space and/orthe motion of the punch may be used to animate the user avatar 24 ingame space.

According to other example embodiments, the system 10 may further beused to interpret target movements as operating system and/orapplication controls that are outside the realm of games. For example,virtually any controllable aspect of an operating system and/orapplication may be controlled by movements of the target, such as theuser 18.

In other embodiments, as illustrated in FIG. 1B, a user 18 can performthe motions preceded before a processing device 12A and a capture device20A in a smaller field of vision at a distance closer to the capturedevice than illustrated in FIG. 1A. In the illustration on FIG. 1B, theprocessing device 12A is a notebook computer, and the distance betweenuser 18 and the capture device 20A is much smaller than the embodimentdepicted in FIG. 1A. In addition, because the user is closer to thecapture device, the field of view of the capture device is smaller. Allother elements being equal, a capture device positioned closer to theuser 18 as illustrated in FIG. 1B with a resolution equivalent to thatof capture device 20 in FIG. 1A will have a greater ability to capturethe user's finger and facial movements.

Suitable examples of a system 10 and components thereof are found in thefollowing co-pending patent applications: U.S. patent application Ser.No. 12/475,094 entitled “Environment And/Or Target Segmentation”, filed29 May 2009; U.S. patent application Ser. No. 12/511,850, entitled “AutoGenerating a Visual Representation”, filed 29 Jul. 2009; U.S. patentapplication Ser. No. 12/474,655, “Gesture Tool” filed on May 29, 2009;U.S. patent application Ser. No. 12/603,437, “Pose Tracking Pipeline,”filed on Oct. 21, 2009, (hereinafter referred to as the '437application); U.S. patent application Ser. No. 12/475,308, “Device forIdentifying and Tracking Multiple Humans Over Time,” filed on May 29,2009; “Motion Detection Using Depth Images,” filed on Dec. 18, 2009; andU.S. patent application Ser. No. 12/575,388, “Human Tracking System,”filed on Oct. 7, 2009; U.S. patent application Ser. No. 12/422,661,“Gesture Recognizer System Architecture,” filed on Apr. 13, 2009; U.S.patent application Ser. No. 12/391,150, “Standard Gestures,” filed onFeb. 23, 2009; and U.S. patent application Ser. No. 12/474,655, “GestureTool” filed on May 29, 2009.

FIG. 2 illustrates an example embodiment of a capture device 20 that maybe used for target recognition, analysis, and tracking in a scene, wherethe target can be a user or an object. According to an exampleembodiment, the capture device 20 may be configured to capture videowith depth information including a depth image that may include depthvalues via any suitable technique including, for example,time-of-flight, structured light, stereo image, or the like. Accordingto one embodiment, the capture device 20 may organize the calculateddepth information into “Z layers,” or layers that may be perpendicularto a Z axis extending from the depth camera along its line of sight.

As shown in FIG. 2, the capture device 20 may include an image cameracomponent 32. According to an example embodiment, the image cameracomponent 32 may be a depth camera that may capture the depth image of ascene. The depth image may include a two-dimensional (2-D) pixel area ofthe captured scene where each pixel in the 2-D pixel area may representa depth value such as a length or distance in, for example, centimeters,millimeters, or the like of an object in the captured scene from thecamera.

As shown in FIG. 2, according to an example embodiment, the image cameracomponent 32 may include an IR light component 34, a first sensor suchas a three-dimensional (3-D) camera 36, and a second sensor such as anRGB camera 38 that may be used to capture the depth image of a scene.Each of these components is focused on a scene. For example, intime-of-flight analysis, the IR light component 34 of the capture device20 may emit an infrared light onto the scene and may then use sensors(not shown) to detect the backscattered light from the surface of one ormore targets and objects in the scene using, for example, the a 3-Dcamera 26 and/or the RGB camera 38. In some embodiments, pulsed infraredlight may be used such that the time between an outgoing light pulse anda corresponding incoming light pulse may be measured and used todetermine a physical distance from the capture device 20 to a particularlocation on the targets or objects in the scene. Additionally, in otherexample embodiments, the phase of the outgoing light wave may becompared to the phase of the incoming light wave to determine a phaseshift. The phase shift may then be used to determine a physical distancefrom the capture device 20 to a particular location on the targets orobjects.

According to another example embodiment, time-of-flight analysis may beused to indirectly determine a physical distance from the capture device20 to a particular location on the targets or objects by analyzing theintensity of the reflected beam of light over time via varioustechniques including, for example, shuttered light pulse imaging.

In another example embodiment, the capture device 20 may use astructured light to capture depth information. In such an analysis,patterned light (i.e., light displayed as a known pattern such as gridpattern or a stripe pattern) may be projected onto the scene via, forexample, the IR light component 34. Upon striking the surface of one ormore targets or objects in the scene, the pattern may become deformed inresponse. Such a deformation of the pattern may be captured by, forexample, the 3-D camera 36 and/or the RGB camera 38 and may then beanalyzed to determine a physical distance from the capture device 20 toa particular location on the targets or objects.

According to another embodiment, the capture device 20 may include twoor more physically separated cameras or sensors that may view a scenefrom different angles, to obtain visual stereo data that may be resolvedto generate depth information.

In another example embodiment, the capture device 20 may use point clouddata and target digitization techniques to detect features of the user.

The capture device 20 may further include a microphone 40, or an arrayof microphones. The microphone 30 may include a transducer or sensorthat may receive and convert sound into an electrical signal. Accordingto one embodiment, the microphone 30 may be used to reduce feedbackbetween the capture device 20 and the computing environment 12 in thetarget recognition, analysis, and tracking system 10. Additionally, themicrophone 30 may be used to receive audio signals that may also beprovided by the user to control applications such as game applications,non-game applications, or the like that may be executed by the computingenvironment 12.

In an example embodiment, the capture device 20 may further include aprocessor or microcontroller 42 that may be in operative communicationwith the image camera component 32. The processor 42 may include astandardized processor, a specialized processor, a microprocessor, orthe like that may execute instructions that may include instructions forreceiving the depth image, determining whether a suitable target may beincluded in the depth image, converting the suitable target into askeletal representation or model of the target, or any other suitableinstruction.

The capture device 20 may further include a memory component 44 that maystore the instructions that may be executed by the microcontroller 42,images or frames of images captured by the 3-d camera 36 or RGB camera38, or any other suitable information, images, or the like. According toan example embodiment, the memory component 44 may include random accessmemory (RAM), read only memory (ROM), cache, Flash memory, a hard disk,or any other suitable storage component. Together, the microcontroller42 and memory may be collectively referred to as a microcontroller.

As shown in FIG. 2, in one embodiment, the memory component 44 may be aseparate component in communication with the image capture component 32and the processor 42. According to another embodiment, the memorycomponent 44 may be integrated into the processor 42 and/or the imagecapture component 32.

As shown in FIG. 2, the capture device 20 may be in communication withthe computing environment 12 via a communication link 40. Thecommunication link 46 may be a wired connection including, for example,a USB connection, a Firewire connection, an Ethernet cable connection,or the like and/or a wireless connection such as a wireless 802.11b, g,a, or n connection. According to one embodiment, the computingenvironment 12 may provide a clock to the capture device 20 that may beused to determine when to capture, for example, a scene via thecommunication link 46.

Additionally, the capture device 20 may provide the depth informationand images captured by, for example, the 3-D camera 36 and/or the RGBcamera 38, and a skeletal model that may be generated by the capturedevice 20 to the computing environment 12 via the communication link 46.The computing environment 12 may then use the skeletal model, depthinformation, and captured images to, for example, control an applicationsuch as a game or word processor.

Computing environment 12 may include components such as thoseillustrated in FIGS. 3 and 4 to enable operation of applications, suchas a boxing application or a sign language interpreter 180, to beperformed thereon.

In FIG. 2, illustrated in computing system 12 are a gesture recognizer190 and a sign language interpreter 180. In one embodiment, the gesturerecognizer 190 may comprise, for example, a skeletal extractioncomponent 192, a motion tracker 196, a registration component 194, aface classifier 198, and hand classifier 199. The skeletal extractioncomponent 192 functions in accordance with U.S. patent applicationSerial Ser. No. 12/475,094 to extract and define a skeletal system totrack user motion. Examples of skeletal systems are illustrated in FIGS.10A and 10C. The motion tracking component 186 operates in conjunctionwith the disclosure of the '437 application to track the motion of thedetected skeleton within a scene. Motions and gesture components aretranslated into gestures which are matched against a library 193 ofknown signs which are equivalent to gestures. Gesture componentsinclude, but are not limited to: hand shape and configuration relativeto a user's body and other hand; finger shape and configuration relativeto a user's hand, other fingers and body; hand and finger orientation(e.g. up, down, sideways); hand, finger arm and head movement includingthe beginning and ending positions of the movement relative to otherhand, finger, arm and body positions (e.g. across the chest, off to theside, etc.). This is illustrated in FIG. 10A below. The registrationcomponent 194 synchronizes the information provided by the components34, 36, 38, 40, of capture device 20. Information from the capturedevice may, as discussed above, include depth and image information.Registration component 194 synchronizes this information to detectgesture movement.

Face classifier 198 and hand classifier 199 detect fine-grained changesin a users hand and face, hand and finger shape as well asconfiguration, orientation, position and movement, all of which canaffect the interpretation of a gesture as described below. Detection offace expression and a individual digit movements of a hand may berelevant to the interpretation of a gesture as a sign as illustrated inFIGS. 5 and 6. Face classifier 198 and hand classifier 199 work inconjunction with skeletal extraction component 192, a motion tracker196. The skeletal extraction component 192, a motion tracker 196 informthe face classifier 198 and hand classifier 199 where the hands and faceare located in the scene so that the hand and face classifiers are notburdened with determining that for themselves. The skeletal extractioncomponent 192 also uniquely identifies each user so that each user'ssign language conversations can be tracked independently.

Where the resolution of the capture device 20 is sufficient to providetracking of a model of a hand or face, face classifier 198 and handclassifier 199 determine positions of the users face and hands based onmotions of the face and hands that add information to the matchingalgorithm of the a Lexicon/grammar matcher 195, both of which detect theuser 18 in a scene based on the information provided by the capturedevice 20 to provide a sign language output 188. The Lexicon/Grammermatcher 195 may include a lexicon dictionary 193, user data 186 and agrammar library 185. When a gesture is detected, the information is fedto the Lexicon/grammar matcher 195 which consults the dictionary 193 andcompares detected motions to those stored in the dictionary to determinethe meaning of particular signs provided by the user. This is describedbelow with respect to FIG. 7 and may be enabled by one or more hardwarecomponents and a processor which is specifically programmed to executeinstructions to accomplish the techniques described herein. In addition,signs assigned to gestures are compared to the grammar library 185 anduser data 186 to verify the accuracy of the assignment of the sign tothe gesture. The grammar library 185 contains information on whether anysign makes sense in light of preceding and succeeding signs. User data186 contains user specific demographics and other user-specificinformation used to determine if the sign makes sense in view ofspecific known user information.

The target recognition, analysis and tracking system 10 may determinewhether the depth image includes a human target. In one embodiment, theedges of each target such as the human target and the non-human targetsin the captured scene of the depth image may be determined. As describedabove, each of the depth values may represent a depth value such as alength or distance in, for example, centimeters, millimeters, or thelike of an object in the captured scene from the capture device 20.According to an example embodiment, the edges may be determined bycomparing various depth values associated with, for example, adjacent ornearby pixels of the depth image. If the various depth values beingcompared are greater than a predetermined edge tolerance, the pixels maydefine an edge.

According to another embodiment, predetermined points or areas on thedepth image may be flood filled to determine whether the depth imageincludes a human target. For example, various depth values of pixels ina selected area or point of the depth image may be compared to determineedges that may define targets or objects as described above. In anexample embodiment, the predetermined points or areas may be evenlydistributed across the depth image. For example, the predeterminedpoints or areas may include a point or an area in the center of thedepth image, two points or areas in between the left edge and the centerof the depth image, two points or areas between the right edge and thecenter of the depth image, or the like.

The Z values of the Z layers may be flood filled based on the determinededges. For example, the pixels associated with the determined edges andthe pixels of the area within the determined edges may be associatedwith each other to define a target or an object in the capture area thatmay be compared with a pattern.

According to an example embodiment, each of the flood-filled targets,human and non-human may be matched against a pattern to determinewhether and/or which of the targets in the capture area include a human.The pattern may include, for example, a machine representation of apredetermined body model associated with a human in various positions orposes such as a typical standing pose with arms to each side.

In an example embodiment, the human target may be isolated and a bitmaskof the human target may be created to scan for one or more body parts.For example, after a valid human target is found within the depth image,the background or the area of the depth image not matching the humantarget can be removed. A bitmask may then be generated for the humantarget that may include values of the human target along, for example,an X, Y, and Z axis. According to an example embodiment, the bitmask ofthe human target may be scanned for various body parts, starting with,for example, the head to generate a model of the human target. The topof the bitmask may be associated with a location of the top of the head.After determining the top of the head, the bitmask may be scanneddownward to then determine a location of a neck, a location ofshoulders, and the like. The depth map or depth image data can beupdated to include a probability that a pixel is associated with aparticular virtual body part in the model.

According to an example embodiment, upon determining the values of abody part, a data structure may be created that may include measurementvalues such as length, width, or the like of the body part associatedwith the bitmask of the human target. In one embodiment, the datastructure for the body part may include results averaged from aplurality of depth images captured in frames by the capture system 60 ata frame rate. The model may be iteratively adjusted at a certain numberof frames. According another embodiment, the measurement values of thedetermined body parts may be adjusted such as scaled up, scaled down, orthe like such that measurements values in the data structure moreclosely correspond to a typical model of a human body. A body model maycontain any number of body parts, each of which may be anymachine-understandable representation of the corresponding part of themodeled target.

In a model example including two or more body parts, each body part ofthe model may comprise one or more structural members (i.e., “bones”),with joints located at the intersection of adjacent bones. For example,measurement values determined by the bitmask may be used to define oneor more joints in a skeletal model (illustrated below with respect toFIG. 10A, for example). The one or more joints may be used to define oneor more bones that may correspond to a body part of a human. Each jointmay allow one or more body parts to move relative to one or more otherbody parts. For example, a model representing a human target may includea plurality of rigid and/or deformable body parts, wherein some bodyparts may represent a corresponding anatomical body part of the humantarget. Each body part may be characterized as a mathematical vectordefining joints and bones of the skeletal model. It is to be understoodthat some bones may correspond to anatomical bones in a human targetand/or some bones may not have corresponding anatomical bones in thehuman target.

The bones and joints may collectively make up a skeletal model (FIG.10A, 10D, 10E), which may be a constituent element of another model. Theskeletal model may include one or more skeletal members for each bodypart and a joint between adjacent skeletal members.

As the user moves in physical space, as captured by capture device 20,the resultant image data may be used to adjust the skeletal model suchthat the skeletal model may accurately represent the user. According toan example embodiment, the model can be rasterized into a synthesizeddepth image. Rasterization allows the model described by mathematicalvectors, polygonal meshes, or other objects to be converted into asynthesized depth image described in terms of pixels. Differencesbetween an observed image of the target, as retrieved by a capturesystem, and a rasterized (i.e., synthesized) image of the model may beused to determine the force vectors that are applied to the model inorder to adjust the body into a different pose. In one embodiment, oneor more force vectors may be applied to one or more force-receivingaspects of a model to adjust the model into a pose that more closelycorresponds to the pose of the target in the physical space of thecapture area. The model may be iteratively adjusted as frames arecaptured. Depending on the type of model that is being used, the forcevector may be applied to a joint, a centroid of a body part, a vertex ofa triangle, or any other suitable force-receiving aspect of the model.Furthermore, in some embodiments, two or more different calculations maybe used when determining the direction and/or magnitude of the force.

In one or more embodiments for capturing a user's natural movements, thecapture device 20 repeatedly sends data for motion tracking to thecomputing system 12. The motion tracking data may include datareferenced with respect to some form of a skeletal model such as vectorswith respect to different joints, centroids or nodes to illustratemovement changes. The data may be referenced to a synthesized pixel datarepresentation created from rasterizing the vector data. The data mayalso include a bitmask of the user for comparison on each update todetect which body parts are moving. Each body part is indexed so it canbe identified, other parts of the capture area such as the furniture inthe living room are identified as background, and the users are indexedso the machine representable data for their respective body parts can belinked to them.

The motion tracker 195 can use indices to identify which body parts havechanged position between updates. For different body parts, there areassociated gesture filters in a gesture library 455. A gesture filterincludes instructions for determining whether the movements indicated inthe update or a series of updates represents a gesture, which can be amovement itself or a resulting pose. The gestures can have a meaningwith respect to a gesture based sign language, as described below.

In one embodiment, a gesture filter 450 (FIG. 10B) executes instructionscomparing motion tracking data for one or more body parts involved withthe gesture with parameters including criteria relating to motioncharacteristics which define the gesture. Some examples of motioncharacteristics include position, angle, speed and acceleration changesin a users hand and face, hand and finger shape as well asconfiguration, orientation, position and movement. For instance, athrow, may be implemented as a gesture comprising informationrepresenting the movement of one of the hands of the user from behindthe rear of the body to past the front of the body, as that movementwould be captured by a depth camera. Some examples of a parameter forthe “throw” may be a threshold velocity that the hand has to reach, adistance the hand must travel (either absolute, or relative to the sizeof the user as a whole), and a direction of movement of the hand frombehind the body to past its front. The parameters can be stored asmetadata for its corresponding gesture. A parameter may comprise any ofa wide variety of motion characteristics for a gesture. Where the filtercomprises a parameter, the parameter value can take different forms, forexample, it may be a threshold, an absolute value, a fault tolerance ora range.

Some more examples of motion characteristics that may be represented byparameters are as follows: body parts involved in the gesture, angles ofmotion with respect to a body part, a joint, other body parts or acenter of gravity of the user's body as represented by his skeletalmodel, changes in position of a body part or whole body, and distancesmoved by a body part or whole body. Additionally, other examples ofcharacteristics are a location of a volume of space around the user'sbody in which a body part moves, a direction of movement, a velocity ofmovement of a body part, a place where a movement occurs, an anglebetween a body part and another object in the scene, an accelerationthreshold, the time period of the gesture, the specific time of thegesture, and a release point.

In an embodiment, the user also uses his voice to make, augment,distinguish or clarify a gesture.

In one example, a gesture filter's criteria references a skeletal modellike one or more of those shown in FIGS. 10A, 10D, 10E. The filter maycomprise code and associated data that can process depth values, orvectors with respect to the skeletal data, or color image data or acombination of two or more of these when determining whether theparameter criteria are satisfied. For example, inputs to a filter maycomprise things such as joint data about a user's joint position, anglesformed by the bones that meet at the joint, RGB color data of a userwhich can be helpful for collision testing, and the rate of change of anaspect of the user. The input data may be presented as changes occur inposition, speed, direction of movement, joint angle etc. with a previouspositioning data set for the one or more body parts involved in thegesture.

Whether there is a match or not can be represented by one or moreoutputted confidence levels. In one example, the confidence level couldbe implemented on a linear scale that ranges over floating point numbersbetween 0 and 1, inclusive. In an embodiment, determining the confidencelevel may comprise a boolean determination based on the parametersassociated with the filter. For example, each parameter may have its ownassociated confidence level that the motion characteristic associatedwith it is being made and which the motion tracker 196 may retrieve forits gesture determination. A weighting may be given each parameter andits confidence level which may be used by a weighting technique todetermine a confidence level that the gesture as a whole is being made.Additionally, there may be an output of a motion characteristic for agiven gesture. Examples of a motion characteristic include a time,speed, acceleration rate or angle at which a gesture is made.

More information about recognizer engine 190 can be found in U.S. patentapplication Ser. No. 12/422,661, “Gesture Recognizer SystemArchitecture,” filed on Apr. 13, 2009. More information aboutrecognizing gestures can be found in U.S. patent application Ser. No.12/391,150, “Standard Gestures,” filed on Feb. 23, 2009; and U.S. patentapplication Ser. No. 12/474,655, “Gesture Tool” filed on May 29, 2009.

As used herein, a computing environment 12 may refer to a singlecomputing device or to a computing system. The computing environment mayinclude non-computing components. The computing environment may includeor be connected to a display device which displays an output. A displaydevice may be an entity separate but coupled to the computingenvironment or the display device may be the computing device thatprocesses and displays such as a notebook. Thus, a computing system,computing device, computing environment, computer, processor, or othercomputing component may be used interchangeably.

FIG. 3 illustrates an example embodiment of a computing system that maybe the computing system 12 shown in FIGS. 1A-2 used to track motionand/or animate (or otherwise update) an avatar or other on-screen objectdisplayed by an application. The computing system such as the computingsystem 12 described above with respect to FIGS. 1A-2 may be a multimediaconsole 100, such as a gaming console. As shown in FIG. 3, themultimedia console 100 has a central processing unit (CPU) 101 having alevel 1 cache 102, a level 2 cache 104, and a flash ROM (Read OnlyMemory) 106. The level 1 cache 102 and a level 2 cache 104 temporarilystore data and hence reduce the number of memory access cycles, therebyimproving processing speed and throughput. The CPU 101 may be providedhaving more than one core, and thus, additional level 1 and level 2caches 102 and 104. The flash ROM 106 may store executable code that isloaded during an initial phase of a boot process when the multimediaconsole 100 is powered on.

A graphics processing unit (GPU) 108 and a video encoder/video codec(coder/decoder) 114 form a video processing pipeline for high speed andhigh resolution graphics processing. Data is carried from the graphicsprocessing unit 108 to the video encoder/video codec 114 via a bus. Thevideo processing pipeline outputs data to an A/V (audio/video) port 140for transmission to a television or other display. A memory controller110 is connected to the GPU 108 to facilitate processor access tovarious types of memory 112, such as, but not limited to, a RAM (RandomAccess Memory).

The multimedia console 100 includes an I/O controller 120, a systemmanagement controller 122, an audio processing unit 123, a networkinterface controller 124, a first USB host controller 126, a second USBcontroller 128 and a front panel I/O subassembly 130 that areimplemented on a module 118. The USB controllers 126 and 128 serve ashosts for peripheral controllers 142(1)-142(2), a wireless adapter 148,and an external memory device 146 (e.g., flash memory, external CD/DVDROM drive, removable media, etc.). The network interface 124 and/orwireless adapter 148 provide access to a network (e.g., the Internet,home network, etc.) and may be any of a wide variety of various wired orwireless adapter components including an Ethernet card, a modem, aBluetooth module, a cable modem, and the like.

System memory 143 is provided to store application data that is loadedduring the boot process. A media drive 144 is provided and may comprisea DVD/CD drive, Blu-Ray drive, hard disk drive, or other removable mediadrive, etc. The media drive 144 may be internal or external to themultimedia console 100. Application data may be accessed via the mediadrive 144 for execution, playback, etc. by the multimedia console 100.The media drive 144 is connected to the I/O controller 120 via a bus,such as a Serial ATA bus or other high speed connection (e.g., IEEE1394).

The system management controller 122 provides a variety of servicefunctions related to assuring availability of the multimedia console100. The audio processing unit 123 and an audio codec 132 form acorresponding audio processing pipeline with high fidelity and stereoprocessing. Audio data is carried between the audio processing unit 123and the audio codec 132 via a communication link. The audio processingpipeline outputs data to the A/V port 140 for reproduction by anexternal audio user or device having audio capabilities.

The front panel I/O subassembly 130 supports the functionality of thepower button 150 and the eject button 152, as well as any LEDs (lightemitting diodes) or other indicators exposed on the outer surface of themultimedia console 100. A system power supply module 136 provides powerto the components of the multimedia console 100. A fan 138 cools thecircuitry within the multimedia console 100.

The CPU 101, GPU 108, memory controller 110, and various othercomponents within the multimedia console 100 are interconnected via oneor more buses, including serial and parallel buses, a memory bus, aperipheral bus, and a processor or local bus using any of a variety ofbus architectures. By way of example, such architectures can include aPeripheral Component Interconnects (PCI) bus, PCI-Express bus, etc.

When the multimedia console 100 is powered on, application data may beloaded from the system memory 143 into memory 112 and/or caches 102, 104and executed on the CPU 101. The application may present a graphicaluser interface that provides a consistent user experience whennavigating to different media types available on the multimedia console100. In operation, applications and/or other media contained within themedia drive 144 may be launched or played from the media drive 144 toprovide additional functionalities to the multimedia console 100.

The multimedia console 100 may be operated as a standalone system bysimply connecting the system to a television or other display. In thisstandalone mode, the multimedia console 100 allows one or more users tointeract with the system, watch movies, or listen to music. However,with the integration of broadband connectivity made available throughthe network interface 124 or the wireless adapter 148, the multimediaconsole 100 may further be operated as a participant in a larger networkcommunity.

When the multimedia console 100 is powered ON, a set amount of hardwareresources are reserved for system use by the multimedia consoleoperating system. These resources may include a reservation of memory(e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking bandwidth(e.g., 8 kbs), etc. Because these resources are reserved at system boottime, the reserved resources do not exist from the application's view.

In particular, the memory reservation is large enough to contain thelaunch kernel, concurrent system applications and drivers. The CPUreservation is constant such that if the reserved CPU usage is not usedby the system applications, an idle thread will consume any unusedcycles.

With regard to the GPU reservation, lightweight messages generated bythe system applications (e.g., pop ups) are displayed by using a GPUinterrupt to schedule code to render popup into an overlay. The amountof memory required for an overlay depends on the overlay area size andthe overlay scales with screen resolution. Where a full user interfaceis used by the concurrent system application, it is preferable to use aresolution independent of application resolution.

After the multimedia console 100 boots and system resources arereserved, concurrent system applications execute to provide systemfunctionalities. The system functionalities are encapsulated in a set ofsystem applications that execute within the reserved system resourcesdescribed above. The operating system kernel identifies threads that aresystem application threads versus gaming application threads. The systemapplications are scheduled to run on the CPU 101 at predetermined timesand intervals in order to provide a consistent system resource view tothe application. The scheduling is to minimize cache disruption for thegaming application running on the console.

When a concurrent system application requires audio, audio processing isscheduled asynchronously to the gaming application due to timesensitivity. A multimedia console application manager (described below)controls the gaming application audio level (e.g., mute, attenuate) whensystem applications are active.

Input devices (e.g., controllers 142(1) and 142(2)) are shared by gamingapplications and system applications. The input devices are not reservedresources, but are to be switched between system applications and thegaming application such that each will have a focus of the device. Theapplication manager controls the switching of input stream, withoutknowledge the gaming application's knowledge and a driver maintainsstate information regarding focus switches. The cameras 36, 38 andcapture device 20 may define additional input devices for the console100 via USB controller 126 or other interface.

FIG. 4 illustrates another example embodiment of a computing system 220that may be used to implement the computing system 12 shown in FIGS. 1-2to track motion and/or animate (or otherwise update) an avatar or otheron-screen object displayed by an application. The computing systemenvironment 220 is only one example of a suitable computing system andis not intended to suggest any limitation as to the scope of use orfunctionality of the presently disclosed subject matter. Neither shouldthe computing system 220 be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin the exemplary operating system 220. In some embodiments the variousdepicted computing elements may include circuitry configured toinstantiate specific aspects of the present disclosure. For example, theterm circuitry used in the disclosure can include specialized hardwarecomponents configured to perform function(s) by firmware or switches. Inother examples embodiments the term circuitry can include a generalpurpose processing unit, memory, etc., configured by softwareinstructions that embody logic operable to perform function(s). Inexample embodiments where circuitry includes a combination of hardwareand software, an implementer may write source code embodying logic andthe source code can be compiled into machine readable code that can beprocessed by the general purpose processing unit. Since one skilled inthe art can appreciate that the state of the art has evolved to a pointwhere there is little difference between hardware, software, or acombination of hardware/software, the selection of hardware versussoftware to effectuate specific functions is a design choice left to animplementer. More specifically, one of skill in the art can appreciatethat a software process can be transformed into an equivalent hardwarestructure, and a hardware structure can itself be transformed into anequivalent software process. Thus, the selection of a hardwareimplementation versus a software implementation is one of design choiceand left to the implementer.

Computing system 220 comprises a computer 241, which typically includesa variety of computer readable media. Computer readable media can be anyavailable media that can be accessed by computer 241 and includes bothvolatile and nonvolatile media, removable and non-removable media. Thesystem memory 222 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 223and random access memory (RAM) 260. A basic input/output system 224(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 241, such as during start-up, istypically stored in ROM 223. RAM 260 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 259. By way of example, and notlimitation, FIG. 4 illustrates operating system 225, applicationprograms 226, other program modules 227, and program data 228.

The computer 241 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 4 illustrates a hard disk drive 238 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 239that reads from or writes to a removable, nonvolatile magnetic disk 254,and an optical disk drive 240 that reads from or writes to a removable,nonvolatile optical disk 253 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 238 is typically connectedto the system bus 221 through an non-removable memory interface such asinterface 234, and magnetic disk drive 239 and optical disk drive 240are typically connected to the system bus 221 by a removable memoryinterface, such as interface 235.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 4, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 241. In FIG. 4, for example, hard disk drive 238 is illustratedas storing operating system 258, application programs 257, other programmodules 256, and program data 255. Note that these components can eitherbe the same as or different from operating system 225, applicationprograms 226, other program modules 227, and program data 228. Operatingsystem 258, application programs 257, other program modules 256, andprogram data 255 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation into the computer 241 through input devices such as akeyboard 251 and pointing device 252, commonly referred to as a mouse,trackball or touch pad. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit259 through a user input interface 236 that is coupled to the systembus, but may be connected by other interface and bus structures, such asa parallel port, game port or a universal serial bus (USB). The cameras36, 38 and capture device 20 may define additional input devices for theconsole 100 that connect via user input interface 236. A monitor 242 orother type of display device is also connected to the system bus 221 viaan interface, such as a video interface 232. In addition to the monitor,computers may also include other peripheral output devices such asspeakers 244 and printer 243, which may be connected through a outputperipheral interface 233. Capture Device 20 may connect to computingsystem 220 via output peripheral interface 233, network interface 237,or other interface.

The computer 241 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer246. The remote computer 246 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 241, although only a memory storage device 247 has beenillustrated in FIG. 4. The logical connections depicted include a localarea network (LAN) 245 and a wide area network (WAN) 249, but may alsoinclude other networks. Such networking environments are commonplace inoffices, enterprise-wide computer networks, intranets and the Internet.

When used in a LAN networking environment, the computer 241 is connectedto the LAN 245 through a network interface or adapter 237. When used ina WAN networking environment, the computer 241 typically includes amodem 250 or other means for establishing communications over the WAN249, such as the Internet. The modem 250, which may be internal orexternal, may be connected to the system bus 221 via the user inputinterface 236, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 241, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 5 illustrates applicationprograms 248 as residing on memory device 247. It will be appreciatedthat the network connections shown are exemplary and other means ofestablishing a communications link between the computers may be used.

Either of the systems of FIG. 3 or 4, or a different computing system,can be used to implement Computing System 12 of FIG. 2. As explainedabove, computing system 12 determines the motions of the users andemploys those detected motions to control a video game or otherapplication. For example, a user's motions can be used to control anavatar and/or object in a video game. In some embodiments, the systemcan simultaneously track multiple users and allow the motion of multipleusers to control or effect the application.

The system will use the RGB images and depth images to track a user'smovements. For example, the system will track a skeleton of a personusing a depth images. There are many methods that can be used to trackthe skeleton of a person using depth images. One suitable example oftracking a skeleton using depth images is provided in U.S. patentapplication Ser. No. 12/603,437, “Pose Tracking Pipeline,” filed on Oct.21, 2009. (hereinafter referred to as the '437 application). The processof the '437 application includes acquiring a depth image, down samplingthe data, removing and/or smoothing high variance noisy data,identifying and removing the background, and assigning each of theforeground pixels to different parts of the body. Based on those steps,the system will fit a model with the data and create a skeleton. Theskeleton will include a set of joints and connections between thejoints.

In one embodiment, in order for a user's motion to be used to control anapplication the user must first be enrolled or bound to the application.In one embodiment, each user will be asked to identify himself orherself by standing in front of the system so that depth images and/orvisual images can be obtained from multiple angles for that user. Forexample, the user may be asked to stand in front of the camera, turnaround, and make various poses while depth images and visual images areobtained. After the system obtains enough depth and/or visual images,the system will create a set of identifying data from the images thatuniquely identifies the user. The system will create a uniqueidentification and associate that unique identification with on-screenrepresentation (e.g., avatar) or other object in the game/application.After a user is enrolled in (or bound to) the application, the systemwill track the motion of that user while the user is actively engagedwith the application (e.g., playing the game or using the application).However, in the past, other people in the room who are not activelyengaged with the application, (e.g., not bound to application, bound toapplication but not playing current game, or bound to application butcurrently not having a turn to play) do not have a way to interact withthe application.

FIGS. 5A through 5D depict different types of sign language usingAmerican Sign Language which need to be detected by the system. Asdescribed herein, sign language can comprise static signs or signsinvolving motion. A sign language is a language which uses visuallytransmitted gesture or sign to convey meaning. This may include, one ormore of, simultaneous combinations of hand shapes, orientation andmovement of the hands, arms or body, and facial expressions to express aspeaker's thoughts. Sign language may include spatial grammars which aredifferent from the grammars of spoken languages. Hundreds of signlanguages are in use around the world. American Sign Language (or ASL)is the dominant sign language of the United States. In ASL,fingerspelling is used primarily for proper nouns, for emphasis (forexample, fingerspelling STOP is more emphatic than signing ‘stop’), forclarity, and for instruction.

ASL includes both fingerspelling borrowings from English, as well as theincorporation of alphabetic letters from English words into ASL signs todistinguish related meanings of what would otherwise be covered by asingle sign in ASL. For example, two hands trace a circle to mean ‘agroup of people’. Several kinds of groups can be specified by handshape:When made with C hands, the sign means ‘class’; when made with the Fhandshape, it means ‘family’. Such signs are often referred to as“initialized” signs because they substitute the first letter (theinitial) of the corresponding English word as the handshape in order toprovide a more specific meaning.

When using alphabetic letters in these ways, several otherwisenon-phonemic handshapes become distinctive. For example, outsidefingerspelling there is but a single fist handshape—the placement of thethumb is irrelevant. However, within fingerspelling, the position of thethumb on the fist distinguishes the letters A, S, and T.Letter-incorporated signs which rely on such minor distinctions as thumbposition tend not to be stable in the long run, but they may eventuallycreate new distinctions in the language. Signs that may really tooheavily on individual's relative digit sizes and flexibility are notideal. This is analogous to spoken language using sounds that are easilyreplicated by a majority of speakers.

In FIG. 5A, examples of the sign alphabet according to American SignLanguage are shown. The letters “A” 502, “B” 504 and “C” 506 are allmade by static signs, the term static being used here to indicate thatno motion is involved in actually presenting the sign meaning. Contrastwith the letter J shown in FIG. 5B at 508. To make a letter J, the handtakes the form shown at 508 and also takes the motion along with line509 to indicate the letter. Other signs are more complex, involving bothhands and finger motion. FIG. 5C illustrates the sign for “pay” whereuser's right hand moves from position 516 to position 518 along arrow519. The right hand moves relative to the non-moving left hand 520 shownin FIG. 5C. FIG. 5D illustrates an even more complex sign meaning“card.” As illustrated in FIG. 5D, a person's right and left hand 511,513 move along opposite directions 510 and 512 in a motion apart fromeach other after which the user's fingers are pinched as indicated at514.

In order to provide a sign language translation system in accordancewith the present technology, all such types of movements are defined asgestures. The gesture recognizer 190 interprets such gestures, alongwith facial and finger movements and positions, and compares detectedgestures with those in library 193 to provide meaning to each gesture.

Similar signs may appear with different meaning relative to a user'sexpression or head tilt. For example, FIG. 6A illustrates an individualsigning the phrase “father is home.” Each of FIGS. 6A and 6B illustratestwo signs “father” and “home.” The left side of the FIG. 6A indicates auser making the term “father” with the right hand 602 placed against theuser's forehead 610. The motion for “home” as indicated by the usermoving her right hand across her cheek from position 604 to 606. In boththe left and right side of FIG. 6A, the user's face is generallyexpressionless and facing the viewer. FIG. 6B illustrates the samesigns, but posed in an interrogatory manner. The phrase in FIG. 6B isnot a declaration “father is home,” but rather a question “Is fatherhome” or more literally “father is home?” The indication of a phrasemade in the interrogatory comes from the expression of the user and theuser's head tilt. As indicated in FIG. 6B, the signs 602, 604, 606 arethe same, but the user's expression at 614 and 616 with head tiltedslightly to the left and eyebrows raised indicates the expression is aquestion rather than a statement.

FIG. 7 illustrates a method in accordance with the present technologyfor providing a sign language interpretation system based on motiontracking and gesture interpretation. In one embodiment, a user isidentified at 700. While not required, system 10 can store individualprofiles of user motion for specific users. As discussed below, thesystem generates particular skeletal tracking models for a user and themodel and profile information on specific tendencies and motion patternsof the user can be stored with a user profile. Applying known tendenciesto motion and gesture detection for a user may increase the accuracy ofgesture detection and sign language translation.

In one embodiment, a series of calibration steps 701 are performed. Inalternate embodiments, calibration at 701 is not required. At step 702,a gesture calibration motion is displayed to the user. The gesturecalibration motion can require a user to perform a particular gesture sothat the system understands that particular motions used to perform agesture in a particular way by a given user. It should be recognizedthat calibrations for each individual user can be stored by the systemand different users recognize in accordance with the skeletal matchingpattern detected by the processing device 12. Once the calibrationmotion is displayed, the user performs the calibration motion and atstep 704, the user's motions are captured for the calibration event.Steps 702 and 704 can be repeated any number of times to provide adesired accuracy for the system. At step 706, user characteristicinformation may be acquired. User characteristic information maycomprise user demographic such as the user's age, sex, interests,hobbies, favorites, or other information, which can be useful to thesystem in determining the probability that a user is making a particularsign. In one embodiment, characteristic information may be collectedduring the calibration steps by, for example, allowing the user tocomplete a questionnaire on the display. Calibration information may bestored at each of steps 702, 704 and 706 to the user profile.Calibration steps 702 and 704 may be repeated for any number of gesturesand signs. Once calibration steps 701 are performed for a given user,they need not be repeated.

At 708, the scene in a field of view of the capture device 20 ismonitored for user action. As the user makes gestures in the scene, theuser's skeletal model is tracked, and hands and face motions detected,to determine whether a gesture has occurred at 710. User motion istracked and gestures determined in accordance with the discussion belowin FIGS. 10 and 11.

At 712, gestures that are recognized are compared to known sign data andif a possible match is found at 714, an initial sign is assigned to thegesture and an initial probability weight that the gesture is aparticular sign at 716. Gesture motions may have a probability of beingintended by the user as a given sign as well as a number of alternativepossible signs that the gesture may be intended to make. When an initialsign is assigned to a detected gesture (N), any number of possiblealternative signs which may also be potential meanings of the gesturemay be stored in the library. Each sign may have a probability weight orscore relative to a detected gesture. For example, a first alternativefor a gesture may be the letter “A” with a second alternative gesturebeing the letter “B.” Where the user is spelling a proper name, and theuser's name is known, the method can adjust for an error in an initialinterpretation by detecting that “A” is more or less likely to be usedthan “B” given other signs used by the user, and/or known data about theuser. Because interpretation of the sign depends on context—user actionsand signs made before and after a particular sign—the method furtherevaluates the initial determination at 714 to confirm or revise theinitial determination based on user profile information and the sign'scontext relative to other signs in a motion stream. At step 718, ifcontext information is available for a particular user, the contextinformation is received at 720.

If the characteristic information is available then at 722, comparisonis made between the user's personal characteristics and the initial signwhich is assigned to the recognized gesture. If the characteristicinformation indicates that the sign is not correct, the sign may becorrected at 724. Comparison of the characteristic information to theinitial sign is discussed below at FIG. 8. If the characteristicinformation confirms the initial assignment of the sign evaluation at722, additionally, and optionally, the sign may be compared with othergestures at 728.

A previous gesture is received at 726 and a comparison made at 728 todetermine whether the previous gesture (N−1) confirms the probabilitythat the gesture is the initial sign recognized by the system at 716. Ifso, then the probability weight assigned to the initial sign will beincreased at 736 and an output may be generated at 734 using the initialsign. Any number of different types of outputs may be generated by thesystem, including those illustrated below with respect to FIGS. 12 and13. It should be further recognized that while in the illustrated methodan output is generated at 734 and the sign (N) stored. An output may begenerated at any point after a sign is recognized and assigned to adetected gesture.

It should be recognized that as a user may continually be providingsigns within a scene in a stream of motion. Motions and gestures may beseparated from other motions in a stream by recognizing transitionsbetween signs based on the gesture filter assigned to a gesture and thecontext of the signs made in the motion stream.

At 728, if the previous gesture and sign recognized by the system do notconfirm the initial sign assigned to the gesture, then at 730 adetermination is made as to whether the previous sign in combinationwith one of several alternative possible signs attributable to thegesture justify assigning a new, revised sign to the gesture (N). If anew sign is assigned, the method returns to 716 and assigns a newprobability to the new sign (N) based on revised information from theprevious gesture. If not, then at 732 the probability weighting assignedto the new sign is lowered and the output generated at 724.

As the system is continually receiving gestures and sign, a next sign(N+1) based on another gesture made by the user following the initialgesture will be interpreted and a next gesture will be retrieved at 738.The next gesture will have a probability assigned to it and can be usedto determine whether or not a previously assigned gesture (in this casesign (N)) is or is not likely to be the sign which has been assigned thegesture at 716. At 740 a comparison of the initially assigned sign forsign N is compared with the next sign (N=!) to determine whether thecontext of the next sign confirms the initial sign. A determination of anew sign for the initial sign may be made at 742 and if a new sign isdetermined for the initial sign, the output can be altered at step 742.

FIG. 8 illustrates a process which may be performed at step 726 toevaluate whether the context information confirms the sign assigned tothe gesture. At 802, for each characteristic in a user's personaldemographic information, a determination is made at steps 804, whetherthe demographic increases or decreases the probability that the signassigned to the gesture is correct. If the sign is likely in view of thedemographic, the weight assigned to the sign is increased at 806. Ifnot, the weight is decreased at 808. This continues for each demographicinformation for the individual. It should be noted that not alldemographic information may be relevant to all signs. Demographicinformation may include the user's gender, native language, location,history, favorites, and the like. At 812, once all relevant demographicinformation has been checked against the assigned sign, a determinationis made as to whether alternative signs in the library are a bettermatch based on the probability that such alternative signs have a higherprobability weighting relative to the adjusted weight of the modifiedscore of the assigned sign (N).

FIG. 9 illustrates a process which may occur at steps 728 or 740 todetermine whether a previous sign or next sign assigned to aprevious/next gesture confirms an assigned sign. At 902, theprevious/next gesture and the sign assigned to the previous or nextgesture are retrieved. At step 904, the system consults the dictionaryto determine whether or not additional signs would be likely to beadjacent to the previous gesture. The library may contain a contextualdatabase which includes sign contact information that identifies alikelihood that a particular word, and in this case sign, would appearadjacent to other words or signs. The position of signs relative to eachother may be compared to ascertain that the signs assigned to each ofadjacent gestures make sense in a linguistic context. This can be usedto define sentence structures with a high probability of accuracy. In906, a determination is made as to whether or not the matched sign islikely to be positioned adjacent to the previous or next sign. If so,the assigned sign can be confirmed at 908. If not, a determination ismade at 910 as to whether or not the sign assigned to the gesture wouldnever be adjacent to the previous sign. If so, the sign is rejected at912. If the assigned sign can be adjacent to previous or next sign at910, then at 912 a determination is made as to whether another sign ismore likely to be adjacent to the previous or next sign than theassigned sign. If so, an indication is made to change the sign at 914(which may occur at 730 or 742 in FIG. 7). If not, the sign is confirmedat 916.

FIG. 10A depicts an example skeletal mapping of a user that may begenerated from the capture device 20 in the manner described above. Inthis example, a variety of joints and bones are identified: each hand402, each forearm 404, each elbow 406, each bicep 408, each shoulder410, each hip 412, each thigh 414, each knee 416, each foreleg 418, eachfoot 420, the head 422, the torso 424, the top 426 and bottom 428 of thespine, and the waist 430. Where more points are tracked, additionalfeatures may be identified, such as the bones and joints of the fingersor toes, or individual features of the face, such as the nose and eyes.

Through moving his body, a user may create gestures. A gesture comprisesa motion or pose by a user that may be captured as image data and parsedfor meaning. A gesture may be dynamic, comprising a motion, such asmimicking throwing a ball. A gesture may be a static pose, such asholding one's crossed forearms 404 in front of his torso 424. A gesturemay also incorporate props, such as by swinging a mock sword. A gesturemay comprise more than one body part, such as clapping the hands 402together, or a subtler motion, such as pursing one's lips.

Gestures may be used for input in a general computing context. Forinstance, various motions of the hands 402 or other body parts maycorrespond to common system wide tasks such as navigate up or down in ahierarchical menu structure, scroll items in a menu list, open a file,close a file, and save a file. Gestures may also be used in avideo-game-specific context, depending on the game. For instance, with adriving game, various motions of the hands 402 and feet 420 maycorrespond to steering a vehicle in a direction, shifting gears,accelerating, and breaking.

Gesture parameters may include threshold angles (e.g., hip-thigh angle,forearm-bicep angle, etc.), a number of periods where motion occurs ordoes not occur, a threshold period, threshold position (starting,ending), direction movement, velocity, acceleration, coordination ofmovement, etc.

A gesture may be associated with a set of default parameters that anapplication or operating system may override with its own parameters. Inthis scenario, an application is not forced to provide parameters, butmay instead use a set of default parameters that allow the gesture to berecognized in the absence of application-defined parameters.

There are a variety of outputs that may be associated with the gesture.There may be a baseline “yes or no” as to whether a gesture isoccurring. There also may be a confidence level, which corresponds tothe likelihood that the user's tracked movement corresponds to thegesture. This could be a linear scale that ranges over floating pointnumbers between 0 and 1, inclusive. Where an application receiving thisgesture information cannot accept false-positives as input, it may useonly those recognized gestures that have a high confidence level, suchas at least 0.95. Where an application must recognize every instance ofthe gesture, even at the cost of false-positives, it may use gesturesthat have at least a much lower confidence level, such as those merelygreater than 0.2. The gesture may have an output for the time betweenthe two most recent steps, and where only a first step has beenregistered, this may be set to a reserved value, such as −1 (since thetime between any two steps must be positive). The gesture may also havean output for the highest thigh angle reached during the most recentstep.

Another parameter to a gesture may be a distance moved. Where a user'sgestures control the actions of an avatar in a virtual environment, thatavatar may be arm's length from a ball. If the user wishes to interactwith the ball and grab it, this may require the user to extend his arm402-410 to full length while making the grab gesture. In this situation,a similar grab gesture where the user only partially extends his arm402-410 may not achieve the result of interacting with the ball.

A gesture or a portion thereof may have as a parameter a volume of spacein which it must occur. This volume of space may typically be expressedin relation to the body where a gesture comprises body movement. Forinstance, a football throwing gesture for a right-handed user may berecognized only in the volume of space no lower than the right shoulder410 a, and on the same side of the head 422 as the throwing arm 402a-410 a. It may not be necessary to define all bounds of a volume, suchas with this throwing gesture, where an outer bound away from the bodyis left undefined, and the volume extends out indefinitely, or to theedge of capture area that is being monitored.

FIG. 10B provides further details of one exemplary embodiment of thegesture recognizer engine 190 of FIG. 2. As shown, the gesturerecognizer engine 190 may comprise at least one filter 450 in library450 a to determine a gesture or gestures. A filter 450 comprisesparameters defining a gesture 452 (hereinafter referred to as a“gesture”) along with metadata 454 for that gesture. A filter maycomprise code and associated data that can recognize gestures orotherwise process depth, RGB, or skeletal data. For instance, a throw,which comprises motion of one of the hands from behind the rear of thebody to past the front of the body, may be implemented as a gesture 452comprising information representing the movement of one of the hands ofthe user from behind the rear of the body to past the front of the body,as that movement would be captured by the depth camera. Parameters 454may then be set for that gesture 452. Where the gesture 452 is a throw,a parameter 454 may be a threshold velocity that the hand has to reach,a distance the hand must travel (either absolute, or relative to thesize of the user as a whole), and a confidence rating by the recognizerengine that the gesture occurred. These parameters 454 for the gesture452 may vary between applications, between contexts of a singleapplication, or within one context of one application over time.

A filter may comprise code and associated data that can recognizegestures or otherwise process depth, RGB, or skeletal data. Filters maybe modular or interchangeable. In an embodiment, a filter has a numberof inputs, each of those inputs having a type, and a number of outputs,each of those outputs having a type. In this situation, a first filtermay be replaced with a second filter that has the same number and typesof inputs and outputs as the first filter without altering any otheraspect of the recognizer engine architecture. For instance, there may bea first filter for driving that takes as input skeletal data and outputsa confidence that the gesture associated with the filter is occurringand an angle of steering. Where one wishes to substitute this firstdriving filter with a second driving filter—perhaps because the seconddriving filter is more efficient and requires fewer processingresources—one may do so by simply replacing the first filter with thesecond filter so long as the second filter has those same inputs andoutputs—one input of skeletal data type, and two outputs of confidencetype and angle type.

A filter need not have a parameter. For instance, a “user height” filterthat returns the user's height may not allow for any parameters that maybe tuned. An alternate “user height” filter may have tunableparameters—such as to whether to account for a user's footwear,hairstyle, headwear and posture in determining the user's height.

Inputs to a filter may comprise things such as joint data about a user'sjoint position, like angles formed by the bones that meet at the joint,RGB color data from the capture area, and the rate of change of anaspect of the user. Outputs from a filter may comprise things such asthe confidence that a given gesture is being made, the speed at which agesture motion is made, and a time at which a gesture motion is made.

The gesture recognizer engine 190 may have a base recognizer engine thatprovides functionality to a gesture filter 450. In an embodiment, thefunctionality that the base recognizer engine 456 implements includes aninput-over-time archive that tracks recognized gestures and other input,a Hidden Markov Model implementation (where the modeled system isassumed to be a Markov process—one where a present state encapsulatesany past state information necessary to determine a future state, so noother past state information must be maintained for this purpose—withunknown parameters, and hidden parameters are determined from theobservable data), as well as other functionality required to solveparticular instances of gesture recognition.

Filters 450 are loaded and implemented on top of the base recognizerengine 456 and can utilize services provided by the engine 456 to allfilters 450. In an embodiment, the base recognizer engine 456 processesreceived data to determine whether it meets the requirements of anyfilter 450. Since these provided services, such as parsing the input,are provided once by the base recognizer engine 456 rather than by eachfilter 450, such a service need only be processed once in a period oftime as opposed to once per filter 450 for that period, so theprocessing required to determine gestures is reduced.

An application may use the filters 450 provided by the recognizer engine190, or it may provide its own filter 450, which plugs in to the baserecognizer engine 456. In an embodiment, all filters 450 have a commoninterface to enable this plug-in characteristic. Further, all filters450 may utilize parameters 454, so a single gesture tool as describedbelow may be used to debug and tune the entire filter system. Theseparameters 454 may be tuned for an application or a context of anapplication by a gesture tool.

FIG. 10C illustrates a more fine-grained tracking model used inconjunction with classification of signs made by a hand and arms. A userperforming the gesture for “PAY” is illustrated at in the left hand sideof the figure A corresponding tracking model 470 is illustrated adjacentto the user depicted. The model in FIG. 10C has a high resolution modelthan that illustrated in FIG. 10A. The model in FIG. 10 c includeselements for a user hand 480, wrist 481, and elbow 483 of the user'sright limb and corresponding elements 484-486 for a left limb. Asillustrated therein, when a user moves hand 518 along the motion of line519, the corresponding motion is tracked for at least points 481 (from481 a to 481 b), 482 (from 482 a to 482 b), and 483 (from 483 a to 483b).

FIGS. 10D and 10E illustrate a tracking model used with the signs of thehand. In FIG. 10D, a models may include at least points 804 a-804 m fora hand of a user, as well as a wrist point 808, elbow 806, forearm 802,upper arm 809 and shoulder 810. FIG. 10E illustrates the hand model 800of FIG. 10D showing gestures for the letters “a”, “b”, and “c” using ASLconventions. (Reference numerals omitted in FIG. 10E for clarity.)

FIGS. 11 and 12 illustrate two examples of outputs generated on adisplay 16 by the output generator 188. In FIG. 14, a user is making apay gesture and the word “PAY” may appear at 19 on a display 16. In thiscase the word “PAY” is positioned next to a representation of a user at21 which is provided in a window 27. A representation 21 can be anactual representation of the user, or a generated Avatar in accordancewith the teachings of the U.S. patent application Ser. No. 12/511,850,entitled “Auto Generating a Visual Representation”. As illustrated inFIG. 12, the entire context of a series of gestures can be illustratedat 19. In FIG. 12, the user is completing the sign for late and haspreviously completed a number of gestures which define the words “we areleaving.” Any number of different types of user interfaces may beprovided in accordance with the technology.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims. It is intended that the scopeof the technology be defined by the claims appended hereto.

What is claimed is:
 1. A computer implemented method for interpretingsign language, comprising: capturing a scene using a capture device, thescene including a human target; tracking movements of the human targetin the scene; detecting one or more gestures of the human target in thescene; comparing the one or more gestures to a library of sign languagesigns; and determining a match between the one or more gestures and oneor more sign by adjusting a probability weight of each sign based onacquired individual profiles of user motion for users, and comparingeach probability weight against other signs likely to be assigned to adetected gesture.
 2. The computer implemented method of claim 1 furtherincluding providing an output translating the one or more signs.
 3. Thecomputer implemented method of claim 1 wherein the step of detectingincludes detecting at least a first and a second adjacent gestures, andthe determining includes determining a match of the first and secondadjacent gestures with a first sign and a second sign, respectively. 4.The computer implemented method of claim 3 further including comparingthe first and second signs relative to each other to determine theaccuracy of the match between the first sign and the first gesture. 5.The computer implemented method of claim 4 wherein the step of detectingfurther includes detecting a third sign after the second sign andfurther including comparing the first sign and the third sign to thesecond sign to determine the accuracy of the second sign to the secondgesture.
 6. The computer implemented method of claim 5 wherein each stepof comparing includes determining whether each sign makes sense in alinguistic context.
 7. The computer implemented method of claim 5further including acquiring user demographic information and furtherincluding comparing each match with the user demographic information toverify the accuracy of the match.
 8. The computer implemented method ofclaim 1 wherein the step of determining a match includes assigning aprobability weight indicating the strength of the match between thegesture and the sign.
 9. The computer implemented method of claim 1further including acquiring individual profiles of user motion for usersapplying known tendencies to motion and gesture detection for a user toincrease the accuracy of gesture detection and sign languagetranslation, and determining a match includes applying known tendenciesfor the user to motion and gesture detection and grammaticalinformation.
 10. A computer storage device including instructions forprogramming a processing device to perform a series of steps,comprising: capturing a scene using a capture device, the sceneincluding a human target; tracking movements of the human target in thescene; detecting a plurality of gestures of the human target in thescene; assigning a first sign to a detected gesture by assigning aprobability weight indicating strength of the match between the detectedgesture and the first sign; assigning a second sign to an adjacentdetected gesture by assigning a probability weight indicating thestrength of the match between the adjacent detected gesture and thesecond sign; acquiring individual profiles of user motion for humantargets applying known tendencies to motion and gesture detection foreach human target; and comparing the first sign and the second sign toverify accuracy of the second sign, including applying profileinformation of a detected human target to adjust a probability weightassigned for each sign.
 11. The computer storage medium of claim 10further including outputting a user perceptible translation of the firstand second sign.
 12. The computer storage medium of claim 10 wherein thestep of detecting further includes detecting a third sign after thesecond sign and further including comparing the first sign and the thirdsign to the second sign to determine the accuracy of the second sign tothe adjacent detected gesture.
 13. The computer storage medium of claim12 wherein the step of comparing the first sign and the third sign tothe second sign includes determining whether each sign makes sense in alinguistic context.
 14. The computer storage medium of claim 13 furtherincluding instructions for performing a step of acquiring userdemographic information and comparing each match with the userdemographic information to verify the accuracy of the match.
 15. Thecomputer storage medium of claim 14 wherein each step of assigningincludes determining a match between a detected gesture and a sign, andfurther includes assigning a probability weight to each sign indicatingthe strength of the match between the gesture and the sign.
 16. Thecomputer storage medium of claim 15 wherein the step of comparing thefirst and third sign includes comparing each match with the userdemographic information to verify the accuracy of the match.
 17. Thecomputer storage medium of claim 15 wherein the step of comparingincludes adjusting the probability weight of the second sign based onthe user information and a grammar database and comparing theprobability weight against other signs likely to be assigned to adetected gesture.
 18. An image capture system including: a capturedevice including an RGB sensor and a depth sensor; a host device, thehost device providing a video output, the host device including aprocessor and instructions for programming the processor to perform amethod comprising: tracking movements of a human target in a sceneacquired by the capture device, detecting one or more gestures of thehuman target in the scene; comparing the one or more gestures to alibrary of sign language signs and assigning a first sign to a firstdetected gesture, assigning a second sign to a second detected gesture,and assigning a third sign after the second sign to a third detectedgesture, each said assigning including assigning a probability weightindicating strength of a match between the gesture and each said sign,comparing the first sign and the third sign to the second sign todetermine the accuracy of the second sign to the first and third signs,said comparing including assigning a probability weight to each of thefirst and third signs indicating a strength of the match between thegesture and the sign based on a comparison to the second sign; andacquiring individual profiles of user motion for users applying knowntendencies to motion and gesture detection for a user to increaseaccuracy of gesture detection and sign language translation, each saidcomparing step using information from a human target profile inassigning a probability weight.
 19. The image capture system of claim 18coupled to a display device, further including instructions programmingthe processor to generate an output to a display device indicating atranslation of the first and second sign on the display device.
 20. Thesystem of claim 18 wherein comparing the first sign and the third signto the second sign includes comparing each sign against a grammaticaldatabase to determine a probability that the signs appear adjacent toeach other and wherein the method performed by the processor furtherincludes a step of acquiring user demographic information and whereincomparing the first and third sign includes comparing each match withuser demographic information to verify the accuracy of the match.